Clinical Implications of Altered Drug Transporter Abundance/Function and PBPK Modeling in Specific Populations: An ITC Perspective

Facing the Facts of Altered Plasma Protein Binding: Do Current Models Correctly Predict Changes in Fraction Unbound in Special Populations?

Accounting for variability in plasma protein binding of drugs is an essential input to physiologically-based pharmacokinetic (PBPK) models of special populations. Prediction of fraction unbound in plasma (fu) in such populations typically considers changes in plasma protein concentration while assuming that the binding affinity remains unchanged. A good correlation between predicted vs observed fu data reported for various drugs in a given special population is often used as a justification for such predictive methods. However, none of these analyses evaluated the prediction of the fold-change in fu in special populations relative to the reference population. This would be a more appropriate assessment of the predictivity, analogous to drug-drug interactions. In this study, predictive performance of the single protein binding model was assessed by predicting fu for alpha-1-acid glycoprotein and albumin bound drugs in hepatic impairment, renal impairment, paediatric, elderly, patients with inflammatory disease, and in different ethnic groups for a dataset of >200 drugs. For albumin models, the concordance correlation coefficients for predicted fu were >0.90 for 16 out of 17 populations with sub-groups, indicating strong agreement between predicted and observed values. In contrast, concordance correlation coefficients for predicted fold-change in fu for the same dataset were <0.38 for all populations and sub-groups. Trends were similar for alpha-1-acid glycoprotein models. Accordingly, the predictions of fu solely based on changes in protein concentrations in plasma cannot explain the observed values in some special populations. We recommend further consideration of the impact of changes in special populations to endogenous substances that competitively bind to plasma proteins, and changes in albumin structure due to posttranslational modifications. PBPK models of special populations for highly bound drugs should preferably use measured fu data to ensure reliable prediction of drug exposure or compare predicted unbound drug exposure between populations knowing that these will not be sensitive to changes in fu.

Dealing With Variable Drug Exposure Due to Variable Hepatic Metabolism: A Proof-of-Concept Application of Liquid Biopsy in Renal Impairment

Precision dosing strategies require accounting for between-patient variability in pharmacokinetics (PK), affecting drug exposure, and in pharmacodynamics (PD), affecting response achieved at the same drug concentration at the site of action. Although liquid biopsy for assessing different levels of molecular drug targets has yet to be established, individual characterization of drug elimination pathways using liquid biopsy has recently been demonstrated. The feasibility of applying this approach in conjunction with modeling tools to guide individual dosing remains unexplored. In this study, we aimed to individualize physiologically-based pharmacokinetic (PBPK) models based on liquid biopsy measurements in plasma from 25 donors with different grades of renal function who were previously administered oral midazolam as part of a microdose cocktail. Virtual twin models were constructed based on demographics, renal function, and hepatic expression of relevant pharmacokinetic pathways projected from liquid biopsy output. Simulated exposure (AUC) to midazolam was in agreement with observed data (AFE = 1.38, AAFE = 1.78). Simulated AUC variability with three dosing approaches indicated higher variability with uniform dosing (14-fold) and stratified dosing (13-fold) compared with individualized dosing informed by liquid biopsy (fivefold). Further, exosome screening revealed mRNA expression of 532 targets relevant to drug metabolism and disposition (169 enzymes and 361 transporters). Data related to these targets can be used to further individualize PBPK models for pathways relevant to PK of other drugs. This study provides additional verification of liquid biopsy-informed PBPK modeling approaches, necessary to advance strategies that seek to achieve precise dosing from the start of treatment.

Topotecan and Ginkgolic Acid Inhibit the Expression and Transport Activity of Human Organic Anion Transporter 3 by Suppressing SUMOylation of the Transporter

Organic anion transporter 3 (OAT3), expressed at the basolateral membrane of kidney proximal tubule cells, facilitates the elimination of numerous metabolites, environmental toxins, and clinically important drugs. An earlier investigation from our laboratory revealed that OAT3 expression and transport activity can be upregulated by SUMOylation, a post-translational modification that covalently conjugates SUMO molecules to substrate proteins. Topotecan is a semi-synthetic derivative of the herbal extract camptothecin, approved by the FDA to treat several types of cancer. Ginkgolic acid (GA) is one of the major components in the extract of Ginkgo biloba leaves that has long been used in food supplements for preventing dementia, high blood pressure, and supporting stroke recovery. Both topotecan and GA have been shown to affect protein SUMOylation. In the current study, we tested our hypothesis that topotecan and GA may regulate OAT3 SUMOylation, expression, and transport function. Our data show that the treatment of OAT3-expressing cells with topotecan or GA significantly decreases the SUMOylation of OAT3 by 50% and 75%, respectively. The same treatment also led to substantial reductions in OAT3 expression and the OAT3-mediated transport of estrone sulfate, a prototypical substrate. Such reductions in cell surface expression of OAT3 correlated well with an increased rate of OAT3 degradation. Mechanistically, we discovered that topotecan enhanced the association between OAT3 and the SUMO-specific protease SENP2, a deSUMOylation enzyme, which contributed to the significant decrease in OAT3 SUMOylation. In conclusion, this study unveiled a novel role of topotecan and GA in inhibiting OAT3 expression and transport activity and accelerating OAT3 degradation by suppressing OAT3 SUMOylation. During comorbidity therapies, the use of topotecan or Ginkgo biloba extract could potentially decrease the transport activity of OAT3 in the kidneys, which will in turn affect the therapeutic efficacy and toxicity of many other drugs that are substrates for the transporter.

Pharmacokinetics of long-acting lenacapavir in participants with hepatic or renal impairment

Lenacapavir is a novel, first-in-class, multistage inhibitor of HIV-1 capsid function approved for the treatment of multidrug-resistant HIV-1 infection in combination with other antiretrovirals for heavily treatment-experienced people with HIV. Two Phase 1, open-label, parallel-group, single-dose studies assessed the pharmacokinetics (PK) of lenacapavir in participants with moderate hepatic impairment [Child-Pugh-Turcotte (CPT) Class B: score 7-9] or severe renal impairment [15 ≤ creatinine clearance (CLcr) ≤29 mL/min] to inform lenacapavir dosing in HIV-1-infected individuals with organ impairment. In both studies, a single oral dose of 300 mg lenacapavir was administered to participants with normal (n = 10) or impaired (n = 10) hepatic/renal function who were matched for age (±10 years), sex, and body mass index (±20%). Lenacapavir exposures [area under the plasma concentration-time curve from time 0 to infinity (AUCinf) and maximum concentration (Cmax)] were approximately 1.47- and 2.61-fold higher, respectively, in participants with moderate hepatic impairment compared to those with normal hepatic function, whereas lenacapavir AUCinf and Cmax were approximately 1.84- and 2.62-fold higher, respectively, in participants with severe renal impairment compared to those with normal renal function. Increased lenacapavir exposures with moderate hepatic or severe renal impairment were not considered clinically meaningful. Lenacapavir was considered generally safe and well tolerated in both studies. These results support the use of approved lenacapavir dosing regimen in patients with mild (CPT Class A: score 5-6) or moderate hepatic impairment as well as in patients with mild (60 ≤ CLcr ≤ 89 mL/min), moderate (30 ≤ CLcr ≤ 59 mL/min), and severe renal impairment.

Membrane transporters in drug development and as determinants of precision medicine

The effect of membrane transporters on drug disposition, efficacy and safety is now well recognized. Since the initial publication from the International Transporter Consortium, significant progress has been made in understanding the roles and functions of transporters, as well as in the development of tools and models to assess and predict transporter-mediated activity, toxicity and drug-drug interactions (DDIs). Notable advances include an increased understanding of the effects of intrinsic and extrinsic factors on transporter activity, the application of physiologically based pharmacokinetic modelling in predicting transporter-mediated drug disposition, the identification of endogenous biomarkers to assess transporter-mediated DDIs and the determination of the cryogenic electron microscopy structures of SLC and ABC transporters. This article provides an overview of these key developments, highlighting unanswered questions, regulatory considerations and future directions.

Toward improved predictions of pharmacokinetics of transported drugs in hepatic impairment: Insights from the extended clearance model

Hepatic impairment (HI) moderately (<5-fold) affects the systemic exposure (i.e., area under the plasma concentration-time curve [AUC]) of drugs that are substrates of the hepatic sinusoidal organic anion transporting polypeptide (OATP) transporters and are excreted unchanged in the bile and/or urine. However, the effect of HI on their AUC is much greater (>10-fold) for drugs that are also substrates of cytochrome P450 (CYP) 3A enzymes. Using the extended clearance model, through simulations, we identified the ratio of sinusoidal efflux clearance (CL) over the sum of metabolic and biliary CLs as important in predicting the impact of HI on the AUC of dual OATP/CYP3A substrates. Because HI may reduce hepatic CYP3A-mediated CL to a greater extent than biliary efflux CL, the greater the contribution of the former versus the latter, the greater the impact of HI on drug AUC ratio (AUCRHI ). Using physiologically-based pharmacokinetic modeling and simulation, we predicted relatively well the AUCRHI of OATP substrates that are not significantly metabolized (pitavastatin, rosuvastatin, valsartan, and gadoxetic acid). However, there was a trend toward underprediction of the AUCRHI of the dual OATP/CYP3A4 substrates fimasartan and atorvastatin. These predictions improved when the sinusoidal efflux CL of these two drugs was increased in healthy volunteers (i.e., before incorporating the effect of HI), and by modifying the directionality of its modulation by HI (i.e., increase or decrease). To accurately predict the effect of HI on AUC of hepatobiliary cleared drugs it is important to accurately predict all hepatobiliary pathways, including sinusoidal efflux CL.

Utilization of OATP1B Biomarker Coproporphyrin-I to Guide Drug-Drug Interaction Risk Assessment: Evaluation by the Pharmaceutical Industry

Drug-drug interactions (DDIs) involving hepatic organic anion transporting polypeptides 1B1/1B3 (OATP1B) can be substantial, however, challenges remain for predicting interaction risk. Emerging evidence suggests that endogenous biomarkers, particularly coproporphyrin-I (CP-I), can be used to assess in vivo OATP1B activity. The present work under the International Consortium for Innovation and Quality in Pharmaceutical Development was aimed primarily at assessing CP-I as a biomarker for informing OATP1B DDI risk. Literature and unpublished CP-I data along with pertinent in vitro and clinical DDI information were collected to identify DDIs primarily involving OATP1B inhibition and assess the relationship between OATP1B substrate drug and CP-I exposure changes. Static models to predict changes in exposure of CP-I, as a selective OATP1B substrate, were also evaluated. Significant correlations were observed between CP-I area under the curve ratio (AUCR) or maximum concentration ratio (Cmax R) and AUCR of substrate drugs. In general, the CP-I Cmax R was equal to or greater than the CP-I AUCR. CP-I Cmax R < 1.25 was associated with absence of OATP1B-mediated DDIs (AUCR < 1.25) with no false negative predictions. CP-I Cmax R < 2 was associated with weak OATP1B-mediated DDIs (AUCR < 2). A correlation was identified between CP-I exposure changes and OATP1B1 static DDI predictions. Recommendations for collecting and interpreting CP-I data are discussed, including a decision tree for guiding DDI risk assessment. In conclusion, measurement of CP-I is recommended to inform OATP1B inhibition potential. The current analysis identified changes in CP-I exposure that may be used to prioritize, delay, or replace clinical DDI studies.

Development of 4-Pyridoxic Acid PBPK Model to Support Biomarker-Informed Evaluation of OAT1/3 Inhibition and Effect of Chronic Kidney Disease

Monitoring endogenous biomarkers is increasingly used to evaluate transporter-mediated drug-drug interactions (DDIs) in early drug development and may be applied to elucidate changes in transporter activity in disease. 4-pyridoxic acid (PDA) has been identified as the most sensitive plasma endogenous biomarker of renal organic anion transporters (OAT1/3). Increase in PDA baseline concentrations was observed after administration of probenecid, a strong clinical inhibitor of OAT1/3 and also in patients with chronic kidney disease (CKD). The aim of this study was to develop and verify a physiologically-based pharmacokinetic (PBPK) model of PDA, to predict the magnitude of probenecid DDI and predict the CKD-related changes in PDA baseline. The PBPK model for PDA was first developed in healthy population, building on from previous population pharmacokinetic modeling, and incorporating a mechanistic kidney model to consider OAT1/3-mediated renal secretion. Probenecid PBPK model was adapted from the Simcyp database and re-verified to capture its dose-dependent pharmacokinetics (n = 9 studies). The PBPK model successfully predicted the PDA plasma concentrations, area under the curve, and renal clearance in healthy subjects at baseline and after single/multiple probenecid doses. Prospective simulations in severe CKD predicted successfully the increase in PDA plasma concentration relative to healthy (within 2-fold of observed data) after accounting for 60% increase in fraction unbound in plasma and additional 50% decline in OAT1/3 activity beyond the decrease in glomerular filtration rate. The verified PDA PBPK model supports future robust evaluation of OAT1/3 DDI in drug development and increases our confidence in predicting exposure and renal secretion in patients with CKD.

Drug transporters in drug disposition - the year 2022 in review

On behalf of all the authors, I am pleased to share our third annual review on drug transporter science with an emphasis on articles published and deemed influential in signifying drug transporters' role in drug disposition in the year 2022. As the drug transporter field is rapidly evolving several key findings were noted including promising endogenous biomarkers, rhythmic activity, IVIVE approaches in transporter-mediated clearance, new modality interaction, and transporter effect on gut microbiome. As identified previously (Chothe et Cal. 2021, 2022) the goal of this review is to highlight key findings without a comprehensive overview of each article and to this end, each coauthor independently selected 1-3 peer-reviewed articles published or available online in the year 2022 (Table 1). Each article is summarized in synopsis and commentary with unbiased viewpoints by each coauthor. We strongly encourage readers to consult original articles for specifics of the study. Finally, I would like to thank all coauthors for their continued support in writing this annual review on drug transporters and invite anyone interested in contributing to future versions of this review.

Understanding Statin-Roxadustat Drug-Drug-Disease Interaction Using Physiologically-Based Pharmacokinetic Modeling

A different drug-drug interaction (DDI) scenario may exist in patients with chronic kidney disease (CKD) compared with healthy volunteers (HVs), depending on the interplay between drug-drug and disease (drug-drug-disease interaction (DDDI)). Physiologically-based pharmacokinetic (PBPK) modeling, in lieu of a clinical trial, is a promising tool for evaluating these complex DDDIs in patients. However, the prediction confidence of PBPK modeling in the severe CKD population is still low when nonrenal pathways are involved. More mechanistic virtual disease population and robust validation cases are needed. To this end, we aimed to: (i) understand the implications of severe CKD on statins (atorvastatin, simvastatin, and rosuvastatin) pharmacokinetics (PK) and DDI; and (ii) predict untested clinical scenarios of statin-roxadustat DDI risks in patients to guide suitable dose regimens. A novel virtual severe CKD population was developed incorporating the disease effect on both renal and nonrenal pathways. Drug and disease PBPK models underwent a four-way validation. The verified PBPK models successfully predicted the altered PKs in patients for substrates and inhibitors and recovered the observed statin-rifampicin DDIs in patients and the statin-roxadustat DDIs in HVs within 1.25- and 2-fold error. Further sensitivity analysis revealed that the severe CKD effect on statins PK is mainly mediated by hepatic BCRP for rosuvastatin and OATP1B1/3 for atorvastatin. The magnitude of statin-roxadustat DDI in patients with severe CKD was predicted to be similar to that in HVs. PBPK-guided suitable dose regimens were identified to minimize the risk of side effects or therapeutic failure of statins when co-administered with roxadustat.

Drug-drug interactions and dose management of BTK inhibitors when initiating nirmatrelvir/ritonavir (paxlovid) based on physiologically-based pharmacokinetic models

OBJECTIVE

Co-administration of Bruton's tyrosine kinase (BTK) inhibitors with nirmatrelvir/ritonavir is challenging because of potential drug-drug interactions (DDIs). However, clinical trials specifically evaluating such DDIs are absent. To evaluate and quantify the DDIs between them and provide rational dose management strategies of BTK inhibitors, we conducted this study using physiologically-based pharmacokinetic (PBPK) models.

METHODS

Physicochemical properties and pharmacokinetic parameters were acquired from the published literature and databases. The PBPK models were developed using Simcyp® software. These models were validated by comparing with published literature values. The successfully validated PBPK models were used to simulate the plasma concentration-time profiles and DDIs in a virtual healthy population receiving BTK inhibitors alone or with ritonavir.

RESULTS

Simulated plasma concentration-time profiles and pharmacokinetic parameters of each drug were in agreement with clinically observed values from literatures. Ritonavir increased ibrutinib maximum plasma concentration (Cmax) and the area under plasma concentration-time curve (AUC) 33- and 53.88-fold, respectively, increased zanubrutinib Cmax and AUC 2.57- and 3.18-fold, respectively, and increased acalabrutinib Cmax and AUC 3.85- and 6.54-fold, respectively. Based on our simulations, dose-adjustment strategies may consist of ibrutinib at 25 mg q48h, zanubrutinib at 80 mg twice-daily and acalabrutinib at 25 mg twice-daily with nirmatrelvir/ritonavir.

CONCLUSIONS

The PBPK models predicted the in vivo pharmacokinetics and the DDIs of BTK inhibitors and ritonavir. The prospective simulations not only provided scientific evidence regarding rational dosing management strategies when initiating nirmatrelvir/ritonavir therapy but also provided a reference for the design of clinical DDIs study that may save resources and time.

SUMMARY

Paxlovid could increase Cmax and AUC0-τ of BTK inhibitors (ibrutinib, zanubrutinib and acalabrutinib), and dose adjustment strategy of ibrutinib (25 mg q48h), zanubrutinib (80 mg q12h) and acalabrutinib (25 mg q12h) should be considered when combination with nirmatrelvir/ritonavir.

A 20-Year Research Overview: Quantitative Prediction of Hepatic Clearance Using the In Vitro-In Vivo Extrapolation Approach Based on Physiologically Based Pharmacokinetic Modeling and Extended Clearance Concept

Understanding the extended clearance concept and establishing a physiologically based pharmacokinetic (PBPK) model are crucial for investigating the impact of changes in transporter and metabolizing enzyme abundance/functions on drug pharmacokinetics in blood and tissues. This mini-review provides an overview of the extended clearance concept and a PBPK model that includes transporter-mediated uptake processes in the liver. In general, complete in vitro and in vivo extrapolation (IVIVE) poses challenges due to missing factors that bridge the gap between in vitro and in vivo systems. By considering key in vitro parameters, we can capture in vivo pharmacokinetics, a strategy known as the top-down or middle-out approach. We present the latest progress, theory, and practice of the Cluster Gauss-Newton method, which is used for middle-out analyses. As examples of poor IVIVE, we discuss "albumin-mediated hepatic uptake" and "time-dependent inhibition" of OATP1Bs. The hepatic uptake of highly plasma-bound drugs is more efficient than what can be accounted for by their unbound concentration alone. This phenomenon is referred to as "albumin-mediated" hepatic uptake. IVIVE was improved by measuring hepatic uptake clearance in vitro in the presence of physiologic albumin concentrations. Lastly, we demonstrate the application of Cluster Gauss-Newton method-based analysis to the target-mediated drug disposition of bosentan. Incorporating saturable target binding and OATP1B-mediated hepatic uptake into the PBPK model enables the consideration of nonlinear kinetics across a wide dose range and the prediction of receptor occupancy over time. SIGNIFICANCE STATEMENT: There have been multiple instances where researchers' endeavors to unravel the underlying mechanism of poor in vitro-in vivo extrapolation have led to the discovery of previously undisclosed truths. These include 1) albumin-mediated hepatic uptake, 2) the target-mediated drug disposition in small molecules, and 3) the existence of a trans-inhibition mechanism by inhibitors for OATP1B-mediated hepatic uptake of drugs. Consequently, poor in vitro-in vivo extrapolation and the subsequent inquisitiveness of scientists may serve as a pivotal gateway to uncover hidden mechanisms.

Physiologically-Based Pharmacokinetic Modeling of PAXLOVID™ with First-Order Absorption Kinetics

PURPOSE

PAXLOVID™ is nirmatrelvir tablets co-packaged with ritonavir tablets. Ritonavir is used as a pharmacokinetics (PK) enhancer to reduce metabolism and increase exposure of nirmatrelvir. This is the first disclosure of Paxlovid physiologically-based pharmacokinetic (PBPK) model.

METHODS

Nirmatrelvir PBPK model with first-order absorption kinetics was developed using in vitro, preclinical, and clinical data of nirmatrelvir in the presence and absence of ritonavir. Clearance and volume of distribution were derived from nirmatrelvir PK obtained using a spray-dried dispersion (SDD) formulation where it is considered to be dosed as an oral solution, and absorption is near complete. The fraction of nirmatrelvir metabolized by CYP3A was estimated based on in vitro and clinical ritonavir drug-drug interaction (DDI) data. First-order absorption parameters were established for both SDD and tablet formulation using clinical data. Nirmatrelvir PBPK model was verified with both single and multiple dose human PK data, as well as DDI studies. Simcyp® first-order ritonavir compound file was also verified with additional clinical data.

RESULTS

The nirmatrelvir PBPK model described the observed PK profiles of nirmatrelvir well with predicted AUC and Cmax values within ± 20% of the observed. The ritonavir model performed well resulting in predicted values within twofold of observed.

CONCLUSIONS

Paxlovid PBPK model developed in this study can be applied to predict PK changes in special populations, as well as model the effect of victim and perpetrator DDI. PBPK modeling continues to play a critical role in accelerating drug discovery and development of potential treatments for devastating diseases such as COVID-19. NCT05263895, NCT05129475, NCT05032950 and NCT05064800.

Lamivudine and Emtricitabine Dosing Proposal for Children with HIV and Chronic Kidney Disease, Supported by Physiologically Based Pharmacokinetic Modelling

Dose recommendations for lamivudine or emtricitabine in children with HIV and chronic kidney disease (CKD) are absent or not supported by clinical data. Physiologically based pharmacokinetic (PBPK) models have the potential to facilitate dose selection for these drugs in this population. Existing lamivudine and emtricitabine compound models in Simcyp^® (v21) were verified in adult populations with and without CKD and in non-CKD paediatric populations. We developed paediatric CKD population models reflecting subjects with a reduced glomerular filtration and tubular secretion, based on extrapolation from adult CKD population models. These models were verified using ganciclovir as a surrogate compound. Then, lamivudine and emtricitabine dosing strategies were simulated in virtual paediatric CKD populations. The compound and paediatric CKD population models were verified successfully (prediction error within 0.5- to 2-fold). The mean AUC ratios in children (GFR-adjusted dose in CKD population/standard dose in population with normal kidney function) were 1.15 and 1.23 for lamivudine, and 1.20 and 1.30 for emtricitabine, with grade-3- and -4-stage CKD, respectively. With the developed paediatric CKD population PBPK models, GFR-adjusted lamivudine and emtricitabine dosages in children with CKD resulted in adequate drug exposure, supporting paediatric GFR-adjusted dosing. Clinical studies are needed to confirm these findings.

Quantitative Characterization of Clinically Relevant Drug-Metabolizing Enzymes and Transporters in Rat Liver and Intestinal Segments for Applications in PBPK Modeling

Rats are extensively used as a preclinical model for assessing drug pharmacokinetics (PK) and tissue distribution; however, successful translation of the rat data requires information on the differences in drug metabolism and transport mechanisms between rats and humans. To partly fill this knowledge gap, we quantified clinically relevant drug-metabolizing enzymes and transporters (DMETs) in the liver and different intestinal segments of Sprague-Dawley rats. The levels of DMET proteins in rats were quantified using the global proteomics-based total protein approach (TPA) and targeted proteomics. The abundance of the major DMET proteins was largely comparable using quantitative global and targeted proteomics. However, global proteomics-based TPA was able to detect and quantify a comprehensive list of 66 DMET proteins in the liver and 37 DMET proteins in the intestinal segments of SD rats without the need for peptide standards. Cytochrome P450 (Cyp) and UDP-glycosyltransferase (Ugt) enzymes were mainly detected in the liver with the abundance ranging from 8 to 6502 and 74 to 2558 pmol/g tissue. P-gp abundance was higher in the intestine (124.1 pmol/g) as compared to that in the liver (26.6 pmol/g) using the targeted analysis. Breast cancer resistance protein (Bcrp) was most abundant in the intestinal segments, whereas organic anion transporting polypeptides (Oatp) 1a1, 1a4, 1b2, and 2a1 and multidrug resistance proteins (Mrp) 2 and 6 were predominantly detected in the liver. To demonstrate the utility of these data, we modeled digoxin PK by integrating protein abundance of P-gp and Cyp3a2 into a physiologically based PK (PBPK) model constructed using PK-Sim software. The model was able to reliably predict the systemic as well as tissue concentrations of digoxin in rats. These findings suggest that proteomics-informed PBPK models in preclinical species can allow mechanistic PK predictions in animal models including tissue drug concentrations.

Evaluating the impact of co-administered drug and disease on ripretinib exposure: A physiologically-based pharmacokinetic modeling approach

Ripretinib, as an oral kinase inhibitor, has been approved to treat advanced gastrointestinal stromal tumors (GIST) and is often used in combination with other drugs to slow disease progression, thus potential drug-drug Interactions (DDIs) and drug-disease interactions (DDZIs) have received much attention. To guide clinical rational drug use, this study assessed the effect of co-administered drugs and diseases on ripretinib exposure. Simcyp® Simulator was used to develop the physiologically-based pharmacokinetic (PBPK) model of ripretinib, which was validated and refined with clinical data. We then examined the impact of several CYP3A4 inhibitors and inducers as well as different diseases on ripretinib exposure using the validated model. In the DDI simulation, moderate CYP3A4 inhibitors and inducers changed the exposure of ripretinib by 1.25-2 fold. In hepatic impairment (HI), the simulation showed that ripretinib's AUC increased by 32%, 100%, and 152% for Child-Pugh A, B, and C classification while C_max increased by 2%, 10%, and 15%, respectively. In renal impairment (RI), the model-simulated AUC in moderate and severe RIs increased by 27% and 20%. In conclusion, PBPK models demonstrated quantitative prediction of ripretinib's pharmacokinetic changes under varying conditions that might be useful for its rational use.

Predicting changes in the pharmacokinetics of CYP3A-metabolized drugs in hepatic impairment and insights into factors driving these changes

Physiologically based pharmacokinetic models, populated with drug-metabolizing enzyme and transporter (DMET) abundance, can be used to predict the impact of hepatic impairment (HI) on the pharmacokinetics (PK) of drugs. To increase confidence in the predictive power of such models, they must be validated by comparing the predicted and observed PK of drugs in HI obtained by phenotyping (or probe drug) studies. Therefore, we first predicted the effect of all stages of HI (mild to severe) on the PK of drugs primarily metabolized by cytochrome P450 (CYP) 3A enzymes using the default HI module of Simcyp Version 21, populated with hepatic and intestinal CYP3A abundance data. Then, we validated the predictions using CYP3A probe drug phenotyping studies conducted in HI. Seven CYP3A substrates, metabolized primarily via CYP3A (fraction metabolized, 0.7-0.95), with low to high hepatic availability, were studied. For all stages of HI, the predicted PK parameters of drugs were within twofold of the observed data. This successful validation increases confidence in using the DMET abundance data in HI to predict the changes in the PK of drugs cleared by DMET for which phenotyping studies in HI are not available or cannot be conducted. In addition, using CYP3A drugs as an example, through simulations, we identified the salient PK factors that drive the major changes in exposure (area under the plasma concentration-time profile curve) to drugs in HI. This theoretical framework can be applied to any drug and DMET to quickly determine the likely magnitude of change in drug PK due to HI.

Effect of Hepatic Impairment on OATP1B Activity: Quantitative Pharmacokinetic Analysis of Endogenous Biomarker and Substrate Drugs

Hepatic impairment (HI) is known to modulate drug disposition and may lead to elevated plasma exposure. The aim of this study was to quantitate the in vivo OATP1B-mediated hepatic uptake activity in populations with varying degrees of HI. First, we measured baseline levels of plasma coproporphyrin-I, an endogenous OATP1B biomarker, in an open-label, parallel cohort study in adult subjects with normal liver function and mild, moderate, and severe HI (n = 24, 6/cohort). The geometric mean plasma concentrations of coproporphyrin-I were 1.66-fold, 2.81-fold (P < 0.05), and 7.78-fold (P < 0.0001) higher in mild, moderate, and severe impairment than those healthy controls. Second, we developed a dataset of 21 OATP1B substrate drugs with HI data extracted from literature. Median disease-to-healthy plasma area under the curve (AUC) ratios for substrate drugs were ~ 1.4, 3.0, and 6.4 for mild, moderate, and severe HI, respectively. Additionally, significant linear relationship was noted between AUC ratios of substrate drugs without and with co-administration of rifampin, a prototypic OATP1B inhibitor, and AUC ratios in moderate (P < 0.01) and severe (P < 0.001) HI. Third, a physiologically-based pharmacokinetic model analysis was conducted with 10 substrate drugs following estimation of relative OATP1B functional activity in virtual disease population models using coproporphyrin-I data and verification of substrate models with rifampin drug-drug interaction data. This approach adequately predicted plasma AUC change particularly in moderate (9 of 10 within 2-fold) and severe (5 of 5 within 2-fold) HI. Collective findings indicate progressive reduction, by as much as 90-92%, in OATP1B activity in the HI population.

Protein Abundance of Drug Transporters in Human Hepatitis C Livers

Transmembrane drug transport in hepatocytes is one of the major determinants of drug pharmacokinetics. In the present study, ABC transporters (P-gp, MRP1, MRP2, MRP3, MRP4, BCRP, and BSEP) and SLC transporters (MCT1, NTCP, OAT2, OATP1B1, OATP1B3, OATP2B1, OCT1, and OCT3) were quantified for protein abundance (LC-MS/MS) and mRNA levels (qRT-PCR) in hepatitis C virus (HCV)-infected liver samples from the Child–Pugh class A (n = 30), B (n = 21), and C (n = 7) patients. Protein levels of BSEP, MRP3, MCT1, OAT2, OATP1B3, and OCT3 were not significantly affected by HCV infection. P-gp, MRP1, BCRP, and OATP1B3 protein abundances were upregulated, whereas those of MRP2, MRP4, NTCP, OATP2B1, and OCT1 were downregulated in all HCV samples. The observed changes started to be seen in the Child–Pugh class A livers, i.e., upregulation of P-gp and MRP1 and downregulation of MRP2, MRP4, BCRP, and OATP1B3. In the case of NTCP, OATP2B1, and OCT1, a decrease in the protein levels was observed in the class B livers. In the class C livers, no other changes were noted than those in the class A and B patients. The results of the study demonstrate that drug transporter protein abundances are affected by the functional state of the liver in hepatitis C patients.

Utilizing Endogenous Biomarkers to Derisk Assessment of Transporter Mediated Drug-Drug Interactions: A Scientific Perspective

Comprehensive characterization of transporter mediated drug-drug interactions (DDIs) is important to formulate clinical management strategies and ensure the safe and effective use of concomitantly administered drugs. The potential of a drug to inhibit transporters is predicted by comparing the ratio of the relevant concentration (depending on the transporter) and the half maximum inhibitory concentration (IC₅₀ ) to a pre-defined "cut off" value. If the ratio is greater than the cut off value, modeling approaches such as Physiologically Based Pharmacokinetic (PBPK) Modeling or a clinical DDI trial may be recommended. Because false positive (in vitro data suggests the potential for a DDI, whereas no significant DDI is observed in vivo) and false negative (in vitro data does not suggest the potential for a DDI, whereas significant DDI is observed in vivo) outcomes have been observed, there is interest in exploring additional approaches to facilitate prediction of transporter mediated DDIs. The idea of assessing changes in the concentration of endogenous biomarkers (which are substrates of clinically relevant transporters) to gain insight on the potential for a drug to inhibit transporter activity has received widespread attention. This brief report describes how endogenous biomarkers may help to expand the DDI assessment toolkit, highlights some current knowledge gaps, and outlines a conceptual framework that may complement the current paradigm of predicting the potential for transporter mediated DDIs. This article is protected by copyright. All rights reserved.

Physiologically Based Pharmacokinetic Modelling-Guided Dose Management of Oral Anticoagulants when Initiating Paxlovid for COVID-19 Treatment

Patients with coronavirus disease 2019 (COVID‐19) with cardiovascular diseases who are at higher risk of progressing to critical illness should be treated with nirmatrelvir/ritonavir (Paxlovid). Ritonavir, the booster in nirmatrelvir/ritonavir, modulates multiple drug metabolizing enzymes and transporters, complicating its use in real‐world clinics. We aimed to apply physiologically‐based pharmacokinetic (PBPK) modeling to simulate the complex drug–drug interactions (DDIs) of ritonavir with two anticoagulants, rivaroxaban and racemic warfarin, to address this important clinical conundrum. Simulations were implemented within Simcyp Simulator. Compound and population models were adopted from Simcyp and our previous studies. Upon verification and validation of the PBPK model of ritonavir, prospective DDI simulations with the anticoagulants were performed in both the general population (20–65 years) and geriatric subjects (65–85 years) with or without moderate renal impairment. Elevated rivaroxaban concentrations were simulated with nirmatrelvir/ritonavir treatment, where the impact was more profound among geriatric subjects with renal impairment. The overexposure of rivaroxaban was restored to normal range on day 4 post‐discontinuation of nirmatrelvir/ritonavir, corroborating with the recovery of enzyme activity. A lower 10 mg daily dose of rivaroxaban could effectively maintain acceptable systemic exposure of rivaroxaban during nirmatrelvir/ritonavir treatment. Treatment of ritonavir marginally declined simulated S‐warfarin concentrations, but substantially elevated that of R‐warfarin, resulting in a decrease in the international normalized ratio (INR). As INR only recovered 2 weeks post‐nirmatrelvir/ritonavir treatment, a longer surveillance INR for warfarin becomes important. Our PBPK‐guided simulations evaluated clinically important yet untested DDIs and supports clinical studies to ensure proper anticoagulation management of patients with COVID‐19 with chronic coagulative abnormalities when initiating nirmatrelvir/ritonavir therapy.